SPC Basics Training V1 By Carlos Sanchez

Basic Statistical Process Control Training

By Carlos Sanchez

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Content

Quality Improvement & Statistics Variation What is Statistical Process Control? The Normal Distribution Let’s talk about Sigma SPC Tools Control Charts Process Capability

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Why can’t we just take Samples?

Process Control Final Product Sampling

Inspection cost per unit is low Costs are Higher (Take sample, analyze, report, dispose sample)

Inspection not destructive or detrimental to our products

May be destructive or detrimental to our products

Process can be adjusted, stopped, inspected and started up again at a reasonable cost

Process control is not feasible (After the fact) If product is out of spec now we have a full tank/silo

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But the product it’s in Spec!

Meeting the specification is simply NOT ENOUGH, we need a way to know this: How close to the target spec. is our product? How spread out were the results?

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Variation

Assignable Variation: Variation caused by factors that can be clearly identified and possibly managed

Common Variation: Random variation which is caused by the production process

Example: A poorly trained employee that creates variation in finished product

output.

Example: A particle classification process that always allows bigger particles to flow

to the finished product

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So, How do we improve?

More Money New & better equipment Flawless Raw Material Luck Reduce Common Variation…How?

Observation, Observation…and more Observation Act on little changes observed Preventive Maintenance Statistical Process Control (SPC) Six Sigma Lean Design of Experiments

Continuous Improvement Tools

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SPC in a nutshell

Minimize needless adjustments in the process (Tweaking) It’s a monitoring tool that lets us know when the process is

changing BEFORE product becomes UNACCEPTABLE/Out Spec/ Unusable.

It’s a prevention tool that allows to detect trends that could lead to defective products. (Early warning system)

Final inspection does not assure quality; remember: “You can’t inspect quality into the product”

Final Inspection is too late downstream

SPC quantifies variability and allowsyou to determine if a process changed

Two things to know about the Normal Distribution

SPREAD

LOCATION: The Center of the

curve isexpressed as the

AVERAGEThis is where the

target Specificationis aimed at

SPREAD or RANGE:The dispersion it is usually expressed as

SIGMA8

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Let’s talk about Sigma

Sigma is just a fancy word for Standard Deviation, which tells

us how far is a particular value fromthe average of the data set.

+/- 1 sigma

+/- 2 sigma

+/- 3 sigma

64.25%

96.45%

99.73%

This is where theinfamous SIX SIGMA

comes from, itmeans sendingProduct in spec.99.73% of the

time

Example

64 Tons

96 Tons

99.7 Tons

Imagine if an upside down bell curve could hold 100 Tons of Cement from a storage silo.

If we are workingat +/- 1 sigmaonly 64 Tons arein Spec.

If we are workingat +/- 2 sigmaonly 96 Tons arein Spec.

If we are workingat +/- 3 sigmaalmost all 100 Tons are in Spec.

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7 Classical SPC Tools

HistogramPareto Chart

Control Chart

Stratification ChartCause Effect Chart

Flow Chart

Check Sheet

For this initialTraining we will focus on:

Control Charts

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Why use Control Charts?

Reduce variation by the systematic elimination of assignable causes

Prevent unnecessary process adjustments (Tweaking) Visually diagnose the process by observing data patterns Find out what our process can do Provide immediate visual feedback Decide if continuing production is worthwhile

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Types of Control Charts

Run Charts for variable data: Individual Chart Mean & Range Charts Std. Dev. Charts

Attribute Charts

We will focus on these today

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Why don’t we just use the Specs. As our Limit?

Too late…It’s bad

Upper Spec.

Lower Spec.

Target

With limits we have a “cushion or safety net” before the S#$@%! Hits the fan!

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So where should these Control Limits be?

+/- 1 sigma

+/- 2 sigma

+/- 3 sigma

64.25%

96.45%

99.73%

Where would you put a Control Limit?

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How is a chart related to the Normal Curve?

Upper Spec.

Lower Spec.

Upper Control Limit

Lower Control Limit

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Let’s tilt the Chart and let the points fall!

Huh!That makes sense!

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At the end it averages out!

When the population is big, looking at individuals to detect trends is tricky…

It’s been proven that when you look at averages these tend to behave like a Normal Curve Google this: Central Limit Theorem (It’s great for those sleepless nights)

So from now on this training all example charts are based on Averages. This means that a “Point” in a control charts represents the “Average” value of a sample (Typical sample size varies from 3 to 5), I like 5, but heck, you can choose whatever size you want

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Usefulness of looking at Average & Range

UCL

LCL

UCL

LCL

R-chart

x-Chart Detects shift

Does notdetect shift

(process mean is shifting upward)

SamplingDistribution

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More Usefulness of looking at Average & Range

UCL

LCL

UCL

LCL

R-chart

x-Chart Does notDetects shift

Detect shift

(process variability is increasing)

SamplingDistribution

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If you really want to plot a chart by hand…Ok!

x Chart Control Limits

UCL = x + A R

LCL = x - A R2

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R Chart Control Limits

UCL = D R

LCL = D R4

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n A2 D3 D42 1.88 0 3.273 1.02 0 2.574 0.73 0 2.285 0.58 0 2.116 0.48 0 2.007 0.42 0.08 1.928 0.37 0.14 1.869 0.34 0.18 1.82

10 0.31 0.22 1.7811 0.29 0.26 1.74

Average of all Averages

Average Range

ConstantsSampleSize

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Let’s Analyze that Chart

Upper Spec.

Lower Spec.

Upper Control Limit

Lower Control Limit

Points out of Control Limits : Rule of thumb, if there are any point outside theControl limits should be investigated.

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Let’s Analyze that Chart

Upper Spec.

Lower Spec.

Upper Control Limit

Lower Control Limit

Trends : Rule of thumb, if there are 7+ points in a row all higher or lowerthan the preceding point

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Let’s Analyze that Chart

Upper Spec.

Lower Spec.

Upper Control Limit

Lower Control Limit

Shifts : Rule of thumb, if there are 5+ points in a row all higher or lowerthan target or Average, this means that the Average has SHIFTED

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Let’s Analyze that Chart

Upper Spec.

Lower Spec.

Upper Control Limit

Lower Control Limit

Cycle : Rule of thumb, if there are 3+ similar peaks or valleys, this is typical ofMachine wear, or dosage cycles…or Tweaking!

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Let’s Analyze that Chart

Upper Spec.

Lower Spec.

Upper Control Limit

Lower Control Limit

Adherence to Center : Rule of thumb, if there are 7+ all smothering the averageor target spec. This means that the measurement equipment is no longer capableof detecting significant variation. This is good, but it signals for improvement in themeasurement system. Maybe the spec can be tightened.

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Let’s Analyze that Chart

Upper Spec.

Lower Spec.

Upper Control Limit

Lower Control Limit

Erratic : Rule of thumb, if there are 6+ points shifting from one extreme of thechart to the other, borderline with the Control Limits, this shows that the processis not stable…When you see this pattern be alert for Non conforming product.

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Let’s talk about Adjustment or Tweaking the Process

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If your process is not capable, then there is a good chance that some of your sample will have values outside the specification. Chances are if you are not looking at a SPC control chart, you may be tempted to make an adjustment. Let's see what would happen.

Adjust Equipment

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Adjust Equipment

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Let’s try to understand Process Capability

Jim. Nice guy too, Works in Plant B as a research assistant, he lives 5 miles from work. In order to get to work he has to get through 5 traffic light onto Hwy 4 (which is frequently backed up by crazy skiers) to downtown Beachtree. There he has to find parking spot, sometimes a couple of blocks away.He is late to work quite frequently.

Jack, Nice guy; works as a technician. He lives 10 miles away from the Plant A. In order to get to work he takes Hwy. 7 and gets off at the Beachtree exit and zips right into work. He never hits any traffic and there is no traffic light between his home and work.

He's never late to work.

Process Capability Continued

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8:06 8:128:007:547:487:42

Late to workEarly to work

JackArrives to work between

7:48 to 7:56 AM.

JimArrives to work between

7:48 to 8:06 AM

If we thought of being early or late to work as our specification, then we can say that Jack is Capable meeting the specification. Jim is Not Capable of meeting the specification.

Tolerance

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Let’s Calculate their Capability to get on Time

JackArrives to work between

7:48 to 7:56 AM 99.7% of time. 6 sigma = 7:56 -7:48 = 8 min.

JimArrives to work between

7:48 to 8:06 AM 99.7% of time. 6 sigma = 8:06 - 7:48 = 18 min.

Tolerance = late - early Tolerance = 8:00 - 7:46 Tolerance = 14 minutes

Capability = Tolerance 6 sigma

Jack's Capability = 14 / 8 = 1.75 (Bill is capable)

If Capability is > 1 then we can conclude that is capable of meeting the spec

Jim's Capability = 14/18 = 0.78 (Jim is NOT capable)

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Ok, lets get to Cp & Cpk…Say what?

OutSpec

Tolerance

Target

Real Avg.

OutSpec

Cp: Measures the capabilityof the process to meet the Tolerance…just like Jack & Jim

Cpk: Measures the capabilityof the process to meet the Target Spec.it looks at the likelihood of making product out spec. So the more“centered” the curve is, a better cpkyou will get.

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More formulas…But they are short!

Cpk = The smallest of:

Target - lower spec or Upper spec - Target 3 sigma 3 sigma

Cp = Upper Spec. – Lower Spec. 6 sigma

Criteria:Both Cp & Cpk should be AT LEAST > 1

Ideally > 1.33Why? Just trust me on this one…

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